Overview

Dataset statistics

Number of variables16
Number of observations29
Missing cells20
Missing cells (%)4.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.6 KiB
Average record size in memory126.2 B

Variable types

Categorical1
Numeric15

Warnings

Total Number of Road Accidents (in numbers) is highly correlated with Total Number of Persons Killed (in numbers) and 11 other fieldsHigh correlation
Total Number of Persons Killed (in numbers) is highly correlated with Total Number of Road Accidents (in numbers) and 11 other fieldsHigh correlation
Total Number of Persons Injured (in numbers) is highly correlated with Total Number of Road Accidents (in numbers) and 11 other fieldsHigh correlation
Population of India (in thousands) is highly correlated with Total Number of Road Accidents (in numbers) and 11 other fieldsHigh correlation
Total Number of Registered Motor Vehicles (in thousands) is highly correlated with Total Number of Road Accidents (in numbers) and 10 other fieldsHigh correlation
Road Length (in kms) is highly correlated with Total Number of Road Accidents (in numbers) and 12 other fieldsHigh correlation
Number of Accidents per Lakh Population is highly correlated with Total Number of Road Accidents (in numbers) and 11 other fieldsHigh correlation
Number of Accidents per Ten Thousand Vehicles is highly correlated with Total Number of Road Accidents (in numbers) and 12 other fieldsHigh correlation
Number of Accidents Per Ten Thousand Kms of Roads is highly correlated with Total Number of Registered Motor Vehicles (in thousands) and 2 other fieldsHigh correlation
Number of Persons Killed Per Lakh Population is highly correlated with Total Number of Road Accidents (in numbers) and 11 other fieldsHigh correlation
Number of Persons Killed Per Ten Thousand Vehicles is highly correlated with Total Number of Road Accidents (in numbers) and 12 other fieldsHigh correlation
Number of Persons Killed per Ten Thousand Kms of Roads is highly correlated with Total Number of Road Accidents (in numbers) and 12 other fieldsHigh correlation
Number of Persons Injured per Lakh Population is highly correlated with Total Number of Road Accidents (in numbers) and 11 other fieldsHigh correlation
Number of Persons Injured Per Ten Thousand Vehicles is highly correlated with Total Number of Road Accidents (in numbers) and 12 other fieldsHigh correlation
Number of Persons Injured Per Ten Thousand Kms of Roads is highly correlated with Number of Accidents per Lakh Population and 6 other fieldsHigh correlation
Total Number of Road Accidents (in numbers) is highly correlated with Years and 14 other fieldsHigh correlation
Years is highly correlated with Total Number of Road Accidents (in numbers) and 14 other fieldsHigh correlation
Population of India (in thousands) is highly correlated with Total Number of Road Accidents (in numbers) and 14 other fieldsHigh correlation
Number of Persons Killed Per Ten Thousand Vehicles is highly correlated with Total Number of Road Accidents (in numbers) and 14 other fieldsHigh correlation
Total Number of Registered Motor Vehicles (in thousands) is highly correlated with Total Number of Road Accidents (in numbers) and 13 other fieldsHigh correlation
Number of Accidents Per Ten Thousand Kms of Roads is highly correlated with Total Number of Road Accidents (in numbers) and 14 other fieldsHigh correlation
Number of Persons Killed Per Lakh Population is highly correlated with Total Number of Road Accidents (in numbers) and 14 other fieldsHigh correlation
Number of Accidents per Ten Thousand Vehicles is highly correlated with Total Number of Road Accidents (in numbers) and 14 other fieldsHigh correlation
Total Number of Persons Killed (in numbers) is highly correlated with Total Number of Road Accidents (in numbers) and 14 other fieldsHigh correlation
Number of Accidents per Lakh Population is highly correlated with Total Number of Road Accidents (in numbers) and 14 other fieldsHigh correlation
Number of Persons Injured per Lakh Population is highly correlated with Total Number of Road Accidents (in numbers) and 14 other fieldsHigh correlation
Number of Persons Injured Per Ten Thousand Vehicles is highly correlated with Total Number of Road Accidents (in numbers) and 14 other fieldsHigh correlation
Number of Persons Injured Per Ten Thousand Kms of Roads is highly correlated with Total Number of Road Accidents (in numbers) and 14 other fieldsHigh correlation
Number of Persons Killed per Ten Thousand Kms of Roads is highly correlated with Total Number of Road Accidents (in numbers) and 14 other fieldsHigh correlation
Road Length (in kms) is highly correlated with Total Number of Road Accidents (in numbers) and 14 other fieldsHigh correlation
Total Number of Persons Injured (in numbers) is highly correlated with Total Number of Road Accidents (in numbers) and 13 other fieldsHigh correlation
Total Number of Registered Motor Vehicles (in thousands) has 2 (6.9%) missing values Missing
Road Length (in kms) has 2 (6.9%) missing values Missing
Number of Accidents per Lakh Population has 1 (3.4%) missing values Missing
Number of Accidents per Ten Thousand Vehicles has 2 (6.9%) missing values Missing
Number of Accidents Per Ten Thousand Kms of Roads has 2 (6.9%) missing values Missing
Number of Persons Killed Per Lakh Population has 1 (3.4%) missing values Missing
Number of Persons Killed Per Ten Thousand Vehicles has 2 (6.9%) missing values Missing
Number of Persons Killed per Ten Thousand Kms of Roads has 2 (6.9%) missing values Missing
Number of Persons Injured per Lakh Population has 2 (6.9%) missing values Missing
Number of Persons Injured Per Ten Thousand Vehicles has 2 (6.9%) missing values Missing
Number of Persons Injured Per Ten Thousand Kms of Roads has 2 (6.9%) missing values Missing
Years is uniformly distributed Uniform
Years has unique values Unique
Total Number of Road Accidents (in numbers) has unique values Unique
Total Number of Persons Killed (in numbers) has unique values Unique
Total Number of Persons Injured (in numbers) has unique values Unique
Population of India (in thousands) has unique values Unique

Reproduction

Analysis started2021-09-30 06:34:24.592129
Analysis finished2021-09-30 06:35:38.620652
Duration1 minute and 14.03 seconds
Software versionpandas-profiling v3.0.0
Download configurationconfig.json

Variables

Years
Categorical

HIGH CORRELATION
UNIFORM
UNIQUE

Distinct29
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size180.0 B
1970
 
1
2006
 
1
2018
 
1
2017
 
1
2016
 
1
Other values (24)
24 

Length

Max length14
Median length4
Mean length4.344827586
Min length4

Characters and Unicode

Total characters126
Distinct characters16
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique29 ?
Unique (%)100.0%

Sample

1st row1970
2nd row1980
3rd row1990
4th row1994
5th row1995

Common Values

ValueCountFrequency (%)
19701
 
3.4%
20061
 
3.4%
20181
 
3.4%
20171
 
3.4%
20161
 
3.4%
20151
 
3.4%
20141
 
3.4%
20131
 
3.4%
20121
 
3.4%
20111
 
3.4%
Other values (19)19
65.5%

Length

2021-09-30T12:05:39.161645image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
19701
 
3.3%
19801
 
3.3%
cagr1
 
3.3%
20181
 
3.3%
20171
 
3.3%
20161
 
3.3%
20151
 
3.3%
20141
 
3.3%
20131
 
3.3%
20121
 
3.3%
Other values (20)20
66.7%

Most occurring characters

ValueCountFrequency (%)
037
29.4%
223
18.3%
121
16.7%
918
14.3%
86
 
4.8%
74
 
3.2%
43
 
2.4%
53
 
2.4%
63
 
2.4%
32
 
1.6%
Other values (6)6
 
4.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number120
95.2%
Uppercase Letter4
 
3.2%
Space Separator1
 
0.8%
Other Punctuation1
 
0.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
037
30.8%
223
19.2%
121
17.5%
918
15.0%
86
 
5.0%
74
 
3.3%
43
 
2.5%
53
 
2.5%
63
 
2.5%
32
 
1.7%
Uppercase Letter
ValueCountFrequency (%)
C1
25.0%
A1
25.0%
G1
25.0%
R1
25.0%
Space Separator
ValueCountFrequency (%)
1
100.0%
Other Punctuation
ValueCountFrequency (%)
/1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common122
96.8%
Latin4
 
3.2%

Most frequent character per script

Common
ValueCountFrequency (%)
037
30.3%
223
18.9%
121
17.2%
918
14.8%
86
 
4.9%
74
 
3.3%
43
 
2.5%
53
 
2.5%
63
 
2.5%
32
 
1.6%
Other values (2)2
 
1.6%
Latin
ValueCountFrequency (%)
C1
25.0%
A1
25.0%
G1
25.0%
R1
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII126
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
037
29.4%
223
18.3%
121
16.7%
918
14.3%
86
 
4.8%
74
 
3.2%
43
 
2.4%
53
 
2.4%
63
 
2.4%
32
 
1.6%
Other values (6)6
 
4.8%

Total Number of Road Accidents (in numbers)
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct29
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean397014.1941
Minimum-0.37
Maximum501423
Zeros0
Zeros (%)0.0%
Negative1
Negative (%)3.4%
Memory size296.0 B
2021-09-30T12:05:39.398664image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum-0.37
5-th percentile129740
Q1373671
median429910
Q3484704
95-th percentile498851.2
Maximum501423
Range501423.37
Interquartile range (IQR)111033

Descriptive statistics

Standard deviation122733.2822
Coefficient of variation (CV)0.3091407914
Kurtosis3.635855801
Mean397014.1941
Median Absolute Deviation (MAD)56239
Skewness-1.920924967
Sum11513411.63
Variance1.506345856 × 1010
MonotonicityNot monotonic
2021-09-30T12:05:39.700651image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
1141001
 
3.4%
4609201
 
3.4%
4670441
 
3.4%
4649101
 
3.4%
4806521
 
3.4%
5014231
 
3.4%
4894001
 
3.4%
4864761
 
3.4%
4903831
 
3.4%
4976861
 
3.4%
Other values (19)19
65.5%
ValueCountFrequency (%)
-0.371
3.4%
1141001
3.4%
1532001
3.4%
2826001
3.4%
3258641
3.4%
3519991
3.4%
3712041
3.4%
3736711
3.4%
3850181
3.4%
3864561
3.4%
ValueCountFrequency (%)
5014231
3.4%
4996281
3.4%
4976861
3.4%
4903831
3.4%
4894001
3.4%
4864761
3.4%
4863841
3.4%
4847041
3.4%
4806521
3.4%
4792161
3.4%

Total Number of Persons Killed (in numbers)
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct29
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean97030.70207
Minimum2.36
Maximum151417
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size296.0 B
2021-09-30T12:05:39.963645image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum2.36
5-th percentile18300
Q176977
median92618
Q3137572
95-th percentile149636.2
Maximum151417
Range151414.64
Interquartile range (IQR)60595

Descriptive statistics

Standard deviation41688.1297
Coefficient of variation (CV)0.4296385454
Kurtosis-0.199286475
Mean97030.70207
Median Absolute Deviation (MAD)28155
Skewness-0.5663237647
Sum2813890.36
Variance1737900158
MonotonicityNot monotonic
2021-09-30T12:05:40.254644image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
145001
 
3.4%
1057491
 
3.4%
1514171
 
3.4%
1479131
 
3.4%
1507851
 
3.4%
1461331
 
3.4%
1396711
 
3.4%
1375721
 
3.4%
1382581
 
3.4%
1424851
 
3.4%
Other values (19)19
65.5%
ValueCountFrequency (%)
2.361
3.4%
145001
3.4%
240001
3.4%
541001
3.4%
644631
3.4%
707811
3.4%
746651
3.4%
769771
3.4%
789111
3.4%
799191
3.4%
ValueCountFrequency (%)
1514171
3.4%
1507851
3.4%
1479131
3.4%
1461331
3.4%
1424851
3.4%
1396711
3.4%
1382581
3.4%
1375721
3.4%
1345131
3.4%
1256601
3.4%

Total Number of Persons Injured (in numbers)
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct29
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean402427.9972
Minimum-1.08
Maximum527512
Zeros0
Zeros (%)0.0%
Negative1
Negative (%)3.4%
Memory size296.0 B
2021-09-30T12:05:40.534653image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum-1.08
5-th percentile85700
Q1375051
median464521
Q3496481
95-th percentile520099
Maximum527512
Range527513.08
Interquartile range (IQR)121430

Descriptive statistics

Standard deviation139021.5881
Coefficient of variation (CV)0.3454570482
Kurtosis2.345492132
Mean402427.9972
Median Absolute Deviation (MAD)55810
Skewness-1.686182169
Sum11670411.92
Variance1.932700194 × 1010
MonotonicityNot monotonic
2021-09-30T12:05:40.829665image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
701001
 
3.4%
4964811
 
3.4%
4694181
 
3.4%
4709751
 
3.4%
4946241
 
3.4%
5002791
 
3.4%
4934741
 
3.4%
4948931
 
3.4%
5096671
 
3.4%
5113941
 
3.4%
Other values (19)19
65.5%
ValueCountFrequency (%)
-1.081
3.4%
701001
3.4%
1091001
3.4%
2441001
3.4%
3115001
3.4%
3232001
3.4%
3695021
3.4%
3750511
3.4%
3783611
3.4%
3906741
3.4%
ValueCountFrequency (%)
5275121
3.4%
5231931
3.4%
5154581
3.4%
5133401
3.4%
5113941
3.4%
5096671
3.4%
5002791
3.4%
4964811
3.4%
4948931
3.4%
4946241
3.4%

Population of India (in thousands)
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct29
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1027087.871
Minimum1.26
Maximum1298043
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size296.0 B
2021-09-30T12:05:41.092648image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1.26
5-th percentile592600
Q1959792
median1079117
Q31208116
95-th percentile1277745
Maximum1298043
Range1298041.74
Interquartile range (IQR)248324

Descriptive statistics

Standard deviation265660.0353
Coefficient of variation (CV)0.2586536584
Kurtosis7.400017491
Mean1027087.871
Median Absolute Deviation (MAD)128999
Skewness-2.360950242
Sum29785548.26
Variance7.057525438 × 1010
MonotonicityNot monotonic
2021-09-30T12:05:42.066662image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
5390001
 
3.4%
11121861
 
3.4%
12980431
 
3.4%
12836011
 
3.4%
12689611
 
3.4%
12540191
 
3.4%
12388871
 
3.4%
12235811
 
3.4%
12081161
 
3.4%
12101931
 
3.4%
Other values (19)19
65.5%
ValueCountFrequency (%)
1.261
3.4%
5390001
3.4%
6730001
3.4%
8350001
3.4%
9040001
3.4%
9243591
3.4%
9415791
3.4%
9597921
3.4%
9780811
3.4%
9961301
3.4%
ValueCountFrequency (%)
12980431
3.4%
12836011
3.4%
12689611
3.4%
12540191
3.4%
12388871
3.4%
12235811
3.4%
12101931
3.4%
12081161
3.4%
11767421
3.4%
11608131
3.4%

Total Number of Registered Motor Vehicles (in thousands)
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct27
Distinct (%)100.0%
Missing2
Missing (%)6.9%
Infinite0
Infinite (%)0.0%
Mean93544.37037
Minimum1401
Maximum253311
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size296.0 B
2021-09-30T12:05:42.373667image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1401
5-th percentile8910.3
Q139350
median72718
Q3134806
95-th percentile224028.6
Maximum253311
Range251910
Interquartile range (IQR)95456

Descriptive statistics

Standard deviation71104.88244
Coefficient of variation (CV)0.760119312
Kurtosis-0.3427760288
Mean93544.37037
Median Absolute Deviation (MAD)42233
Skewness0.8025079011
Sum2525698
Variance5055904307
MonotonicityStrictly increasing
2021-09-30T12:05:42.638646image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
45211
 
3.4%
2533111
 
3.4%
2300311
 
3.4%
2100231
 
3.4%
1907041
 
3.4%
1815081
 
3.4%
1594911
 
3.4%
1418661
 
3.4%
1277461
 
3.4%
1149511
 
3.4%
Other values (17)17
58.6%
(Missing)2
 
6.9%
ValueCountFrequency (%)
14011
3.4%
45211
3.4%
191521
3.4%
276601
3.4%
302951
3.4%
337861
3.4%
373321
3.4%
413681
3.4%
448751
3.4%
488571
3.4%
ValueCountFrequency (%)
2533111
3.4%
2300311
3.4%
2100231
3.4%
1907041
3.4%
1815081
3.4%
1594911
3.4%
1418661
3.4%
1277461
3.4%
1149511
3.4%
1053531
3.4%

Road Length (in kms)
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct27
Distinct (%)100.0%
Missing2
Missing (%)6.9%
Infinite0
Infinite (%)0.0%
Mean3808985.963
Minimum1188728
Maximum5897671
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size296.0 B
2021-09-30T12:05:42.908664image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1188728
5-th percentile1639471.2
Q13262503
median3621507
Q34629638.5
95-th percentile5563948.3
Maximum5897671
Range4708943
Interquartile range (IQR)1367135.5

Descriptive statistics

Standard deviation1192628.587
Coefficient of variation (CV)0.3131092103
Kurtosis-0.05678284517
Mean3808985.963
Median Absolute Deviation (MAD)646472
Skewness-0.2399128005
Sum102842621
Variance1.422362946 × 1012
MonotonicityNot monotonic
2021-09-30T12:05:43.182649image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
14918731
 
3.4%
58976711
 
3.4%
56032931
 
3.4%
54721441
 
3.4%
54024861
 
3.4%
52319221
 
3.4%
48653941
 
3.4%
46768381
 
3.4%
45824391
 
3.4%
44715101
 
3.4%
Other values (17)17
58.6%
(Missing)2
 
6.9%
ValueCountFrequency (%)
11887281
3.4%
14918731
3.4%
19838671
3.4%
28909501
3.4%
29750351
3.4%
32025151
3.4%
32283561
3.4%
32966501
3.4%
32987881
3.4%
33160781
3.4%
ValueCountFrequency (%)
58976711
3.4%
56032931
3.4%
54721441
3.4%
54024861
3.4%
52319221
3.4%
48653941
3.4%
46768381
3.4%
45824391
3.4%
44715101
3.4%
41095921
3.4%

Number of Accidents per Lakh Population
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct23
Distinct (%)82.1%
Missing1
Missing (%)3.4%
Infinite0
Infinite (%)0.0%
Mean38.04642857
Minimum21.2
Maximum42.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size296.0 B
2021-09-30T12:05:43.468652image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum21.2
5-th percentile26.65
Q138.05
median39.4
Q340.225
95-th percentile42.43
Maximum42.5
Range21.3
Interquartile range (IQR)2.175

Descriptive statistics

Standard deviation4.982077667
Coefficient of variation (CV)0.130947315
Kurtosis6.894603279
Mean38.04642857
Median Absolute Deviation (MAD)1.25
Skewness-2.577756657
Sum1065.3
Variance24.82109788
MonotonicityNot monotonic
2021-09-30T12:05:43.696664image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
39.43
 
10.3%
42.52
 
6.9%
362
 
6.9%
39.82
 
6.9%
21.21
 
3.4%
36.21
 
3.4%
37.91
 
3.4%
401
 
3.4%
39.51
 
3.4%
40.61
 
3.4%
Other values (13)13
44.8%
ValueCountFrequency (%)
21.21
3.4%
22.81
3.4%
33.81
3.4%
362
6.9%
36.21
3.4%
37.91
3.4%
38.11
3.4%
38.31
3.4%
38.61
3.4%
38.81
3.4%
ValueCountFrequency (%)
42.52
6.9%
42.31
3.4%
41.91
3.4%
41.41
3.4%
41.11
3.4%
40.61
3.4%
40.11
3.4%
401
3.4%
39.82
6.9%
39.51
3.4%

Number of Accidents per Ten Thousand Vehicles
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct27
Distinct (%)100.0%
Missing2
Missing (%)6.9%
Infinite0
Infinite (%)0.0%
Mean101.1407407
Minimum18.4
Maximum814.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size296.0 B
2021-09-30T12:05:43.960647image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum18.4
5-th percentile21.8
Q137.1
median59.1
Q396.6
95-th percentile281.51
Maximum814.4
Range796
Interquartile range (IQR)59.5

Descriptive statistics

Standard deviation155.8375566
Coefficient of variation (CV)1.540799044
Kurtosis18.17360799
Mean101.1407407
Median Absolute Deviation (MAD)28.4
Skewness4.085261104
Sum2730.8
Variance24285.34405
MonotonicityStrictly decreasing
2021-09-30T12:05:44.223647image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
338.91
 
3.4%
18.41
 
3.4%
20.91
 
3.4%
23.91
 
3.4%
25.71
 
3.4%
26.81
 
3.4%
30.71
 
3.4%
35.11
 
3.4%
39.11
 
3.4%
42.31
 
3.4%
Other values (17)17
58.6%
(Missing)2
 
6.9%
ValueCountFrequency (%)
18.41
3.4%
20.91
3.4%
23.91
3.4%
25.71
3.4%
26.81
3.4%
30.71
3.4%
35.11
3.4%
39.11
3.4%
42.31
3.4%
461
3.4%
ValueCountFrequency (%)
814.41
3.4%
338.91
3.4%
147.61
3.4%
117.81
3.4%
116.21
3.4%
109.91
3.4%
100.11
3.4%
93.11
3.4%
86.11
3.4%
80.11
3.4%

Number of Accidents Per Ten Thousand Kms of Roads
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct27
Distinct (%)100.0%
Missing2
Missing (%)6.9%
Infinite0
Infinite (%)0.0%
Mean1098.214815
Minimum788.3
Maximum1424.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size296.0 B
2021-09-30T12:05:44.510647image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum788.3
5-th percentile872.23
Q11017.4
median1152.6
Q31185.15
95-th percentile1199.61
Maximum1424.5
Range636.2
Interquartile range (IQR)167.75

Descriptive statistics

Standard deviation136.3298678
Coefficient of variation (CV)0.124137706
Kurtosis0.591243875
Mean1098.214815
Median Absolute Deviation (MAD)40.5
Skewness-0.3753836737
Sum29651.8
Variance18585.83285
MonotonicityNot monotonic
2021-09-30T12:05:44.758652image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
1026.91
 
3.4%
788.31
 
3.4%
857.81
 
3.4%
916.31
 
3.4%
905.91
 
3.4%
929.81
 
3.4%
1007.91
 
3.4%
1064.21
 
3.4%
1090.31
 
3.4%
1087.71
 
3.4%
Other values (17)17
58.6%
(Missing)2
 
6.9%
ValueCountFrequency (%)
788.31
3.4%
857.81
3.4%
905.91
3.4%
916.31
3.4%
929.81
3.4%
959.81
3.4%
1007.91
3.4%
1026.91
3.4%
1064.21
3.4%
1087.71
3.4%
ValueCountFrequency (%)
1424.51
3.4%
1202.41
3.4%
1193.11
3.4%
1192.61
3.4%
1189.21
3.4%
1187.71
3.4%
1187.11
3.4%
1183.21
3.4%
1180.51
3.4%
1179.41
3.4%

Number of Persons Killed Per Lakh Population
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct23
Distinct (%)82.1%
Missing1
Missing (%)3.4%
Infinite0
Infinite (%)0.0%
Mean9.067857143
Minimum2.7
Maximum11.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size296.0 B
2021-09-30T12:05:45.042659image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum2.7
5-th percentile4.615
Q17.9
median8.65
Q311.325
95-th percentile11.765
Maximum11.9
Range9.2
Interquartile range (IQR)3.425

Descriptive statistics

Standard deviation2.387940934
Coefficient of variation (CV)0.2633412609
Kurtosis0.8767871476
Mean9.067857143
Median Absolute Deviation (MAD)1.7
Skewness-0.9052652892
Sum253.9
Variance5.702261905
MonotonicityNot monotonic
2021-09-30T12:05:45.303663image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
11.72
 
6.9%
7.92
 
6.9%
8.22
 
6.9%
8.12
 
6.9%
11.42
 
6.9%
2.71
 
3.4%
10.51
 
3.4%
11.51
 
3.4%
11.91
 
3.4%
11.31
 
3.4%
Other values (13)13
44.8%
ValueCountFrequency (%)
2.71
3.4%
3.61
3.4%
6.51
3.4%
7.11
3.4%
7.71
3.4%
7.81
3.4%
7.92
6.9%
81
3.4%
8.12
6.9%
8.22
6.9%
ValueCountFrequency (%)
11.91
3.4%
11.81
3.4%
11.72
6.9%
11.51
3.4%
11.42
6.9%
11.31
3.4%
11.21
3.4%
10.81
3.4%
10.51
3.4%
10.11
3.4%

Number of Persons Killed Per Ten Thousand Vehicles
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct26
Distinct (%)96.3%
Missing2
Missing (%)6.9%
Infinite0
Infinite (%)0.0%
Mean18.65555556
Minimum5.8
Maximum103.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size296.0 B
2021-09-30T12:05:45.560647image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum5.8
5-th percentile6.72
Q110.25
median12.7
Q319.95
95-th percentile45.63
Maximum103.5
Range97.7
Interquartile range (IQR)9.7

Descriptive statistics

Standard deviation19.45334326
Coefficient of variation (CV)1.042764082
Kurtosis14.68034983
Mean18.65555556
Median Absolute Deviation (MAD)5.1
Skewness3.610611813
Sum503.7
Variance378.4325641
MonotonicityNot monotonic
2021-09-30T12:05:45.837647image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
11.82
 
6.9%
11.71
 
3.4%
5.81
 
3.4%
6.61
 
3.4%
71
 
3.4%
7.31
 
3.4%
7.61
 
3.4%
8.71
 
3.4%
101
 
3.4%
10.51
 
3.4%
Other values (16)16
55.2%
(Missing)2
 
6.9%
ValueCountFrequency (%)
5.81
3.4%
6.61
3.4%
71
3.4%
7.31
3.4%
7.61
3.4%
8.71
3.4%
101
3.4%
10.51
3.4%
10.91
3.4%
11.41
3.4%
ValueCountFrequency (%)
103.51
3.4%
53.11
3.4%
28.21
3.4%
23.41
3.4%
23.31
3.4%
22.11
3.4%
20.61
3.4%
19.31
3.4%
18.31
3.4%
16.21
3.4%

Number of Persons Killed per Ten Thousand Kms of Roads
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct27
Distinct (%)100.0%
Missing2
Missing (%)6.9%
Infinite0
Infinite (%)0.0%
Mean250.8703704
Minimum122
Maximum304.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size296.0 B
2021-09-30T12:05:46.223651image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum122
5-th percentile179.53
Q1238.9
median250.8
Q3272.6
95-th percentile292.96
Maximum304.7
Range182.7
Interquartile range (IQR)33.7

Descriptive statistics

Standard deviation38.21278295
Coefficient of variation (CV)0.1523208296
Kurtosis4.818768017
Mean250.8703704
Median Absolute Deviation (MAD)17.5
Skewness-1.837971221
Sum6773.5
Variance1460.216781
MonotonicityNot monotonic
2021-09-30T12:05:46.614647image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
160.91
 
3.4%
250.81
 
3.4%
269.11
 
3.4%
2671
 
3.4%
258.51
 
3.4%
262.91
 
3.4%
284.21
 
3.4%
304.71
 
3.4%
293.51
 
3.4%
2811
 
3.4%
Other values (17)17
58.6%
(Missing)2
 
6.9%
ValueCountFrequency (%)
1221
3.4%
160.91
3.4%
2231
3.4%
233.11
3.4%
233.31
3.4%
237.91
3.4%
2381
3.4%
239.81
3.4%
243.71
3.4%
247.11
3.4%
ValueCountFrequency (%)
304.71
3.4%
293.51
3.4%
291.71
3.4%
284.91
3.4%
284.21
3.4%
2811
3.4%
272.71
3.4%
272.51
3.4%
269.11
3.4%
2671
3.4%

Number of Persons Injured per Lakh Population
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct25
Distinct (%)92.6%
Missing2
Missing (%)6.9%
Infinite0
Infinite (%)0.0%
Mean38.18148148
Minimum13
Maximum45.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size296.0 B
2021-09-30T12:05:46.914380image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum13
5-th percentile20.1
Q137.2
median39.8
Q342.4
95-th percentile45.29
Maximum45.7
Range32.7
Interquartile range (IQR)5.2

Descriptive statistics

Standard deviation7.75643837
Coefficient of variation (CV)0.2031466059
Kurtosis5.391180266
Mean38.18148148
Median Absolute Deviation (MAD)2.7
Skewness-2.26214886
Sum1030.9
Variance60.16233618
MonotonicityNot monotonic
2021-09-30T12:05:47.268371image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
39.42
 
6.9%
39.92
 
6.9%
44.61
 
3.4%
36.21
 
3.4%
36.71
 
3.4%
39.81
 
3.4%
40.41
 
3.4%
42.21
 
3.4%
42.31
 
3.4%
44.81
 
3.4%
Other values (15)15
51.7%
(Missing)2
 
6.9%
ValueCountFrequency (%)
131
3.4%
16.21
3.4%
29.21
3.4%
34.51
3.4%
351
3.4%
36.21
3.4%
36.71
3.4%
37.71
3.4%
39.11
3.4%
39.21
3.4%
ValueCountFrequency (%)
45.71
3.4%
45.51
3.4%
44.81
3.4%
44.61
3.4%
44.41
3.4%
431
3.4%
42.51
3.4%
42.31
3.4%
42.21
3.4%
411
3.4%

Number of Persons Injured Per Ten Thousand Vehicles
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct27
Distinct (%)100.0%
Missing2
Missing (%)6.9%
Infinite0
Infinite (%)0.0%
Mean85.82962963
Minimum18.6
Maximum500.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size296.0 B
2021-09-30T12:05:47.701391image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum18.6
5-th percentile22.19
Q138.65
median63.9
Q397.9
95-th percentile207.16
Maximum500.4
Range481.8
Interquartile range (IQR)59.25

Descriptive statistics

Standard deviation94.83907753
Coefficient of variation (CV)1.104968971
Kurtosis14.72887245
Mean85.82962963
Median Absolute Deviation (MAD)30.5
Skewness3.569666185
Sum2317.4
Variance8994.450627
MonotonicityNot monotonic
2021-09-30T12:05:47.991379image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
241.31
 
3.4%
18.61
 
3.4%
21.51
 
3.4%
23.81
 
3.4%
25.91
 
3.4%
27.31
 
3.4%
321
 
3.4%
361
 
3.4%
41.31
 
3.4%
44.81
 
3.4%
Other values (17)17
58.6%
(Missing)2
 
6.9%
ValueCountFrequency (%)
18.61
3.4%
21.51
3.4%
23.81
3.4%
25.91
3.4%
27.31
3.4%
321
3.4%
361
3.4%
41.31
3.4%
44.81
3.4%
49.71
3.4%
ValueCountFrequency (%)
500.41
3.4%
241.31
3.4%
127.51
3.4%
112.61
3.4%
109.41
3.4%
106.71
3.4%
101.41
3.4%
94.41
3.4%
83.61
3.4%
81.71
3.4%

Number of Persons Injured Per Ten Thousand Kms of Roads
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct27
Distinct (%)100.0%
Missing2
Missing (%)6.9%
Infinite0
Infinite (%)0.0%
Mean1089.985185
Minimum589.7
Maximum1282.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size296.0 B
2021-09-30T12:05:48.475372image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum589.7
5-th percentile751.49
Q1996.7
median1151.2
Q31215.8
95-th percentile1279.01
Maximum1282.7
Range693
Interquartile range (IQR)219.1

Descriptive statistics

Standard deviation181.7363828
Coefficient of variation (CV)0.1667328926
Kurtosis0.9499366285
Mean1089.985185
Median Absolute Deviation (MAD)79.2
Skewness-1.228923215
Sum29429.6
Variance33028.11285
MonotonicityNot monotonic
2021-09-30T12:05:48.759392image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
731.31
 
3.4%
798.61
 
3.4%
882.71
 
3.4%
914.21
 
3.4%
913.41
 
3.4%
945.91
 
3.4%
1047.51
 
3.4%
1093.51
 
3.4%
1151.21
 
3.4%
1152.81
 
3.4%
Other values (17)17
58.6%
(Missing)2
 
6.9%
ValueCountFrequency (%)
589.71
3.4%
731.31
3.4%
798.61
3.4%
882.71
3.4%
913.41
3.4%
914.21
3.4%
945.91
3.4%
1047.51
3.4%
1077.51
3.4%
1086.41
3.4%
ValueCountFrequency (%)
1282.71
3.4%
1279.41
3.4%
1278.11
3.4%
1273.11
3.4%
1233.11
3.4%
1230.41
3.4%
1221.51
3.4%
1210.11
3.4%
12041
3.4%
1201.21
3.4%

Interactions

2021-09-30T12:04:27.861423image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-30T12:04:28.298419image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-30T12:04:28.771433image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-30T12:04:29.211419image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-30T12:04:29.745430image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-30T12:04:30.229424image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-30T12:04:30.614418image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-30T12:04:31.052441image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-30T12:04:31.411434image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-30T12:04:31.722438image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-30T12:04:32.080426image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-30T12:04:32.440424image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-30T12:04:32.770426image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-30T12:04:33.196424image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-30T12:04:33.570426image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-30T12:04:34.089428image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-30T12:04:34.497980image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-30T12:04:34.883965image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-30T12:04:35.218966image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-30T12:04:35.531958image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-30T12:04:35.922962image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-30T12:04:36.404963image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-30T12:04:38.633963image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-30T12:04:38.964959image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-30T12:04:39.257963image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-30T12:04:39.505964image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-30T12:04:39.741978image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-30T12:04:40.030980image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-30T12:04:40.290971image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-30T12:04:40.579970image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-30T12:04:40.876962image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-30T12:04:41.123979image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-30T12:04:41.374961image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-30T12:04:41.611981image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-30T12:04:41.844964image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-30T12:04:42.108965image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-30T12:04:42.362970image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-30T12:04:42.646963image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-30T12:04:42.895962image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-30T12:04:43.150966image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-30T12:04:43.424967image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-30T12:04:43.693965image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-30T12:04:43.943961image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
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Correlations

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Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
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Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

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A simple visualization of nullity by column.
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Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
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The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
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The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

YearsTotal Number of Road Accidents (in numbers)Total Number of Persons Killed (in numbers)Total Number of Persons Injured (in numbers)Population of India (in thousands)Total Number of Registered Motor Vehicles (in thousands)Road Length (in kms)Number of Accidents per Lakh PopulationNumber of Accidents per Ten Thousand VehiclesNumber of Accidents Per Ten Thousand Kms of RoadsNumber of Persons Killed Per Lakh PopulationNumber of Persons Killed Per Ten Thousand VehiclesNumber of Persons Killed per Ten Thousand Kms of RoadsNumber of Persons Injured per Lakh PopulationNumber of Persons Injured Per Ten Thousand VehiclesNumber of Persons Injured Per Ten Thousand Kms of Roads
01970114100.014500.070100.0539000.01401.01188728.021.2814.4959.82.7103.5122.013.0500.4589.7
11980153200.024000.0109100.0673000.04521.01491873.022.8338.91026.93.653.1160.916.2241.3731.3
21990282600.054100.0244100.0835000.019152.01983867.033.8147.61424.56.528.2272.729.2127.51230.4
31994325864.064463.0311500.0904000.027660.02890950.036.0117.81127.27.123.3223.034.5112.61077.5
41995351999.070781.0323200.0924359.030295.02975035.038.1116.21183.27.723.4237.935.0106.71086.4
51996371204.074665.0369502.0941579.033786.03202515.039.4109.91159.17.922.1233.139.2109.41153.8
61997373671.076977.0378361.0959792.037332.03298788.038.9100.11132.88.020.6233.339.4101.41147.0
71998385018.079919.0390674.0978081.041368.03228356.039.493.11192.68.219.3247.639.994.41210.1
81999386456.081966.0375051.0996130.044875.03296650.038.886.11172.38.218.3248.637.783.61137.7
92000391449.078911.0399265.01014825.048857.03316078.038.680.11180.57.816.2238.039.381.71204.0

Last rows

YearsTotal Number of Road Accidents (in numbers)Total Number of Persons Killed (in numbers)Total Number of Persons Injured (in numbers)Population of India (in thousands)Total Number of Registered Motor Vehicles (in thousands)Road Length (in kms)Number of Accidents per Lakh PopulationNumber of Accidents per Ten Thousand VehiclesNumber of Accidents Per Ten Thousand Kms of RoadsNumber of Persons Killed Per Lakh PopulationNumber of Persons Killed Per Ten Thousand VehiclesNumber of Persons Killed per Ten Thousand Kms of RoadsNumber of Persons Injured per Lakh PopulationNumber of Persons Injured Per Ten Thousand VehiclesNumber of Persons Injured Per Ten Thousand Kms of Roads
192010499628.00134513.00527512.001176742.00127746.04582439.042.539.11090.311.410.5293.544.841.31151.2
202011497686.00142485.00511394.001210193.00141866.04676838.041.135.11064.211.810.0304.742.336.01093.5
212012490383.00138258.00509667.001208116.00159491.04865394.040.630.71007.911.48.7284.242.232.01047.5
222013486476.00137572.00494893.001223581.00181508.05231922.039.826.8929.811.27.6262.940.427.3945.9
232014489400.00139671.00493474.001238887.00190704.05402486.039.525.7905.911.37.3258.539.825.9913.4
242015501423.00146133.00500279.001254019.00210023.05472144.040.023.9916.311.77.0267.039.923.8914.2
252016480652.00150785.00494624.001268961.00230031.05603293.037.920.9857.811.96.6269.136.721.5882.7
262017464910.00147913.00470975.001283601.00253311.05897671.036.218.4788.311.55.8250.836.218.6798.6
272018467044.00151417.00469418.001298043.00NaNNaN36.0NaNNaN11.7NaNNaNNaNNaNNaN
28CAGR 2008/2018-0.372.36-1.081.26NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN